{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from bcolz_array_iterator2 import BcolzArrayIterator2 " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from bcolz import carray" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.arange(14); x" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = np.arange(14); y" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "x = carray(x, chunklen=3)\n", "y = carray(y, chunklen=3)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "b = BcolzArrayIterator2(x, y, shuffle=True, batch_size=3)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "14" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.N" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nit = len(x)//b.batch_size+1; nit" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "for j in range(10000):\n", " bx,by = list(zip(*[next(b) for i in range(nit)]))\n", " nx = np.concatenate(bx)\n", " ny = np.concatenate(by)\n", " assert(np.allclose(nx,ny))\n", " assert(len(np.unique(nx))==len(nx))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[(array([ 9, 10, 11]), array([ 9, 10, 11])),\n", " (array([6, 7, 8]), array([6, 7, 8])),\n", " (array([0, 1, 2]), array([0, 1, 2])),\n", " (array([12, 13]), array([12, 13])),\n", " (array([3, 4, 5]), array([3, 4, 5])),\n", " (array([ 9, 10, 11]), array([ 9, 10, 11])),\n", " (array([0, 1, 2]), array([0, 1, 2])),\n", " (array([3, 4, 5]), array([3, 4, 5])),\n", " (array([6, 7, 8]), array([6, 7, 8])),\n", " (array([12, 13]), array([12, 13])),\n", " (array([12, 13]), array([12, 13])),\n", " (array([6, 7, 8]), array([6, 7, 8])),\n", " (array([3, 4, 5]), array([3, 4, 5])),\n", " (array([0, 1, 2]), array([0, 1, 2])),\n", " (array([ 9, 10, 11]), array([ 9, 10, 11])),\n", " (array([3, 4, 5]), array([3, 4, 5])),\n", " (array([6, 7, 8]), array([6, 7, 8])),\n", " (array([ 9, 10, 11]), array([ 9, 10, 11])),\n", " (array([12, 13]), array([12, 13])),\n", " (array([0, 1, 2]), array([0, 1, 2]))]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[next(b) for i in range(20)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }